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OR Department Faculty Publications

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Books & Chapters

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Technical Reports

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See all OR Department Technical Reports.


Construction of cumulative mean bounds for simulation output

Dashi I. Singham, Michael P. Atkinson

We develop a new measure of reliability, called cumulative mean bounds, that assesses the mean behavior of a process by calculating the probability that the cumulative sample mean will stay below its long-term sample mean, with a given tolerance, over a period of time. In this report, we provide a derivation of a lower bound for the measure when the underlying data are independent and identically distributed with a normal distribution.This derivation provides a preliminary basis for parallel extensions to the two-sided limiting case when we calculate the probability that the sample mean stays within a given distance from the true mean when the assumptions of independence and normality are removed.

A comparison between the 3/9 and the 5/10 watchbills

Nita Lewis Shattuck, Panagiotis Matsangas, Stephanie Brown

This is the second phase of a longitudinal study comparing the fatigue levels, workload, and performance of crewmembers working on the 3-hrs on/9-hrs off (3/9) and the 5-hrs on/10-hrs off (5/10) watchstanding schedules. Crewmembers from the Reactor Department on the USS NIMITZ (N=117, 24.6±3.89 years old, 95 males, 109 enlisted, with 4.25±2.65 years of active duty) participated in this study. Results show that the 3/9 is better than the 5/10 in terms of sleep quality, subjective levels of fatigue, mood, psychomotor vigilance performance, and acceptance by the Sailors. Although crewmembers on both the 5/10 and the 3/9 received, on average, approximately seven hours of sleep per day, the sleep hygiene and acceptance of the two schedules differ considerably.

Cognitive Alignment with Performance Targeted Training Intervention Model: CAPTTIM

Quinn Kennedy, Peter Nesbitt, Jon Alt, Ronald D. Fricker, Jr.

In this technical report, we propose that the use of two simple behavioral measures, in conjunction with neurophysiological measures, can be used to create a training intervention that has the potential to provide: (1) real-time notification as to when a training intervention is needed and (2) real-time information as to the type of training intervention that should be employed. The Cognitive Alignment with Performance Targeted Training Intervention Model (CAPTTIM) determines if a trainee’s cognitive state is aligned or misaligned with actual performance. When misalignment occurs, it indicates that a training intervention is needed. Neurophysiological markers, as captured by eyetracking and electroencephalography (EEG), can assist in determining why misalignment between cognitive state and performance occurred, leading to more effective and targeted training intervention. Because all measures are captured continuously in real time, this model has the potential to increase training efficiency and effectiveness in a variety of training domains. The model is illustrated with two case studies.

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Journal Articles

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Operational Models of Infrastructure Resilience

David L. Alderson, Gerald G. Brown, and Matthew Carlyle

This paper received Society for Risk Analysis Award for Best Paper of 2015 in Risk Analysis.

We propose a definition of infrastructure resilience that is tied to the operation (or function) of an infrastructure as a system of interacting components and that can be objectively evaluated using quantitative models. Specifically, for any particular system, we use quantitative models of system operation to represent the decisions of an infrastructure operator who guides the behavior of the system as a whole, even in the presence of disruptions. Modeling infrastructure operation in this way makes it possible to systematically evaluate the consequences associated with the loss of infrastructure components, and leads to a precise notion of “operational resilience” that facilitates model verification, validation, and reproducible results. Using a simple example of a notional infrastructure, we demonstrate how to use these models for (1) assessing the operational resilience of an infrastructure system, (2) identifying critical vulnerabilities that threaten its continued function, and (3) advising policymakers on investments to improve resilience.


Optimality Functions and Lopsided Convergence

J.O. Royset and R. J-B Wets

Optimality functions pioneered by E. Polak characterize stationary points, quantify the degree with which a point fails to be stationary, and play central roles in algorithm development. For optimization problems requiring approximations, optimality functions can be used to ensure consistency in approximations, with the consequence that optimal and stationary points of the approximate problems indeed are approximately optimal and stationary for an original problem. In this paper, we review the framework and illustrate its application to nonlinear programming and other areas. Moreover, we introduce lopsided convergence of bifunctions on metric spaces and show that this notion of convergence is instrumental in establishing consistency of approximations. Lopsided convergence also leads to further characterizations of stationary points under perturbations and approximations.


Sleep duration increases in rough sea conditions

Panagiotis Matsangas, Nita L. Shattuck, and Michael E. McCauley

Environmental motion can affect shipboard sleep of crewmembers. Slamming and similar harsh motion may interfere with sleep, whereas mild motion and sopite syndrome may enhance sleep. If sleep needs vary by sea condition, this factor should be considered when assessing human performance at sea. The goal of this study was to assess sleep duration in different sea conditions. 


treeClust: An R Package For Tree-Based Clustering Dissimilarities

Samuel E. Buttrey & Lyn R. Whitaker

This paper describes treeClust, an R package that produces dissimilarities useful for clustering. These dissimilarities arise from a set of classification or regression trees, one with each variable in the data acting in turn as a the response, and all others as predictors. This use of trees produces dissimilarities that are insensitive to scaling, benefit from automatic variable selection, and appear to perform well. The software allows a number of options to be set, affecting the set of objects returned in the call; the user can also specify a clustering algorithm and, optionally, return only the clustering vector. The package can also generate a numeric data set whose inter-point distances relate to the treeClust ones; such a numeric data set can be much smaller than the vector of inter-point dissimilarities, a useful feature in big data sets.